Study on the randomness of the peak pressure of spherical blast waves.

In the purpose of exploring the influence of the material property randomness of spherical explosives and media on the randomness of Pm, the peak pressure of spherical blast waves in media, this paper studies the randomness of the peak pressure of spherical blast waves (RPPSBW) in air and water by adopting the DLGABPNN-MCS, a type of Monte Carlo simulation with the operation of DLGABPNN being repeated sampling tests. And in the DLGABPNN, the back propagation neural network (BPNN) optimized by the dynamic lifecycle genetic algorithm (DLGA), the training sample sets and test sample sets are obtained through the repeated numerical simulations of RPPSBW by the one-dimensional spherically-symmetric material point method (1D-SSMPM). This paper, first, takes as the basic random variables the initial density and parameters of the equation of state of the spherical explosive and those of the medium, and considers Pm as the random response, thereby determining the research objective of RPPSBW. This paper proposes the process of employing DLGABPNN-MCS to study RPPSBW, aiming to obtain the statistical characteristics and randomness features of the Pm. Then, with the applied examples in air and water demonstrating the specific procedures of the DLGABPNN-MCS in the RPPSBW study, it proves the capability of the 1D-SSMPM to accurately simulate RPPSBW, the higher efficiency of the DLGA than genetic algorithm in optimizing the BPNN, and the accuracy of the DLGABPNN in calculating the Pm, thus verifying the effectiveness of the DLGABPNN-MCS in the RPPSBW study. It further obtains the statistical characteristics of the Pm such as the mean and coefficient of variation, which reveals the randomness features of the Pm. It is hoped that this paper can shed some light on the future randomness study of other complex blast waves.